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IBIMA Business Review
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Vol. 2013 (2013), Article ID 942446, 20 pages
DOI: 10.5171/2013.942446
Research Article
Database and Data Mining for Coffee
Shops in Egypt
D. F. Rateb
The American University in Cairo (AUC), Egypt
[email protected]
Received Date: 16 April 2013; Accepted Date: 22 July 2013; Published date: 14 October 2013
Academic editor: Katalin Szenes
Copyright © 2013. D. F. Rateb, Distributed under Creative Commons CC-BY 3.0
Abstract
In Egypt the modern as well as the traditional coffee shops are very common and popular.
The traditional “ahwa” existed for centuries and still does. People go there to socialize,
smoke Hookah, and play table games. Now modern coffee shops are becoming quite popular
and very competitive. To ensure survival in such competition, the introduction of business
intelligence systems and the use of database and data mining techniques are preordained.
The objective of this research paper is to present methods, that support the decision making
process by using data mining techniques with MS Excel tools (and its add-ons). Based on the
Rateb study (2013), the ADI modeling approach is used focusing only on the modern coffee
shops in this highly competitive industry.
Keywords: coffee shops, data mining, decision making, Egypt, Rateb’s ADI technique.
Introduction
Coffee or “ahwa” was first introduced in the
Arab world during the 14th century; and it
was spread out to the rest of the world
later on in the 17th century (Francis, 2013).
However, in Egypt it was not served in
public places until it was available in large
quantities and those places or coffee shops
were named “ahwa” after the name of the
beverage (Alpion, Gezim, 2011). Men go
there more than women and they like to
drink tea or coffee/“ahwa”, smoke Hookah
(water pipes or “shisha”), play table games
(e.g. backgammon or dominos), and read
the newspaper (National Geographic,
retrieved 2013). In the 20th century there
was a trend to use them as a place for
political and social debates (Alpion, Gezim,
2011). Also in the 20th century some
modern coffee shops started to emerge in
Egypt such as Cilantro and Starbucks. This
study is only focusing on those modern
coffee shops (note that in some studies the
traditional coffee shops are referred to as
“modern” contrary to this study).
Five very popular coffee shops were
randomly chosen and studied; but only one
helped and knew about this data collection
and that one was Cilantro (the others are
referred to as competitor A, B, C, and D).
Cilantro’s first location was in the elite
Zamalek district followed by the one close
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Cite this Article as: D. F. Rateb (2013), "Database and Data Mining for Coffee Shops in Egypt”,
IBIMA Business Review, Vol. 2013 (2013), Article ID 942446, DOI: 10.5171/2013.942446.
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to the old campus of the American
University in Cairo (AUC) in Egypt.
Competition, on the other hand, led to the
emergence of other “modern” coffee shops
such as Coffee Bean, Costa Coffee, and
Starbucks.
The customer watch started in the summer
of
2010
during
several
weekdays/weekends in five different
coffee shops that were located in five major
districts in Cairo as well as in Giza (all
carried out at the same point in time) and
consuming 1000+ hours of customer
watch. The five districts chosen were City
Stars, Korba, Maadi, Mohandiseen, and
Zamalek. Data collectors were given a
standard format for collecting the data.
Those with laptops had a softcopy to use.
All variables collected were as follows
(note that the variables analyzed and used
in this research are in italics):
•
•
•
•
Gender
Age
Duration
Segment (Tourist, office worker,
shopper, student, other).
• Nationality (local, other)
• What are they doing?
• What did they order? (food,
beverage, take-away food, takeaway beverage, no order)
The shifts studied and used in this research
were (5 hours each):
• Evening shifts from 6 pm to 11 pm.
• Morning shifts from 8 am to 1 pm.
• Afternoon shifts from 1 pm to 6 pm.
The days were chosen:
• Wednesdays (weekday in Egypt)
• Thursday (weekday/weekend in
Egypt)
• Friday (weekend and prayer day in
Egypt)
The data collected was based on variables
agreed upon by Ms. Beshir (Cilantro’s
General Manager at that time). Trying to
collect all the data at once is not ideal as
presented in a study to investigate the
customers of Starbucks (posted by Joseph
Ruiz,
2012).
Here,
for
example,
differentiating between drinks was not
considered; contrary to what was done in
the Starbucks study. This study analyzed
the top most important three variables as
prioritized by Ms. Beshir (displayed above
in italics).
Affinity analysis, Business Intelligence (BI),
data-mining, data warehousing, knowledge
discovery, Knowledge Discovery in
Databases (KDD), and market basket
analysis, are terms that are used
interchangeably in the literature to mean
more or less the same thing (FactPoint
Group, 2008; Goele and Chanana 2012;
Gutierrez, 2013; Kroenke, 2009). Kroenke
(2009) interprets data mining as discovery
driven (KDD), and as a tool (BI) that uses
statistics, mathematics, and artificial
intelligence to help make forecasts. Data
mining is an integral part of knowledge
management (KM) that uses the knowledge
to compete with (Silwattananusarn and
Tuamsuk, 2012). Affinity analysis is used
by Saks to recommend products to
customers based on their purchase history
and other customers who bought the same
thing. Data mining analyzes the data from
different perceptions and provides
information to help reach an optimal
decision to increase revenues or decrease
costs and not just run the business. Market
basket analysis helps in identifying items
that drive the purchase, in classifying the
shopping event as well as comparing the
different
branches/stores
(Gutierrez,
2013). When the uncertainty as well as the
competition is high, the strategic
information systems must be coupled with
techniques such as data mining to
efficiently manage the huge amount of data
(Padhy et al., 2012). Padhy et al., (2012)
warn against trying to come up with one
data mining system that fits all. Data
mining in restaurants with the high
turnover of its managers is very important
in making forecasting decisions (Liu et al.,
2001). Starbucks worldwide accredited the
importance of data mining in this volatile
restaurant industry (Starbucks, 2013).
To search for patterns and relationships
among data, some data mining techniques
are applied using MS Excel tools (and its
add-ons) along with simple formulas done
by the author. From the 8404 customers
that were watched in the five different
districts, almost half of them were Evening
customers. The rest were either Morning or
Afternoon customers with the former being
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two-thirds more than the latter (see left of
Exhibit #1). Note that almost half of the
Food and Beverage items were sold in the
Evening (47%) next to that is the Morning
% of Customers Watched as a
Categorized by Time of Shift
shift amounting to 36% of the items sold
and only 16% of the items were sold during
the Afternoon shift
(see
right
of
Exhibit
#1).
% of Shift Distribution (5 hours
each Evening, Morning, and
Afternoon shift).
% of the Items Sold as a
Categorized by Time of Shift
Exhibit # 1: Shift Distribution in Terms of the Number of Customers (left), Hours per Shift
(middle), and Items Sold per Shift (right)
Regarding the number of female versus
male customers, the difference was
irrelevant with 51% females and 49%
males (contrary to the traditional “ahwa”
as previously noted). Even when analyzed
in terms of shifts, it was not (2001 female
customers versus 2040 male customers
during the evening shift; 1352 versus 1351
and 914 versus 746 for the morning and
afternoon shifts respectively).
Interestingly, the data collected revealed
that from the 8404 customers who were
watched, 5494 customers ordered
Beverage (3825 [67%] customers ordered
Beverage alone –and 1669 [29%]
customers ordered Beverage along with
Food) and 1919 of the customers ordered
Food (250 [4%] customers ordered Food
alone) while 1512 [18%] customers
ordered nothing and 1148 [14%]
customers ordered take-away items.
Further data-mining analysis that was done
revealed that 8036 Food/Beverage items
were sold: 78% of the items sold were
Beverage alone items and 22% were Food
alone items; while 45% of those were
Food+
Beverage
(Exhibit
#2)
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Exhibit #2: % of Food versus Beverage versus (Food+ Beverage) versus NO Order versus
Take-away for All Items Sold (left)/ for Number of Customers (right)
The customers watched are mostly in their
20s (20-30) making 51% of all customers;
however, they account for only 41% of
total sales. Similarly the teenagers (TAs)
order a less percentage of food/beverage
relative to their number. As can be seen
from the following Exhibit, the teenagers
(TAs) account for 9% of the total
Total Number of Customers in 5 Major
Districts during Evening, Morning, and
Afternoon Shifts
customers whereas they order only 8% of
total sales of food/beverage. All other age
groups
order
a
percentage
of
food/beverage items more than their
population count in that sample. For
example the customers who are in their
30s (31-40) make 22% of all customers
and order 24% of all food/beverage items.
Total Number of Items Sold in 5 Major
Districts during Evening, Morning, and
Afternoon Shifts
Exhibit #3: Total Number of Customers Compared with the Total Number of Items Sold
Analyzed by Age Group
Exhibit #4 displays the distribution of
customers per age group, but this time
categorized by the type of order (left three
– Food, Beverage, and Food + Beverage
respectively) as well as the distribution of
items ordered (right three). For example
the top left pie chart shows the percent of
Food customers per age group, the top
right pie chart shows the percentage of
Food items ordered/sold per age group,
the second row on the left pie chart shows
the percent of Beverage customers per age
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group, and on the right the pie chart shows
the percent of Beverage items sold per age
group; and so on. Note that the (20-30)
year old customers are by far the largest
(50% ordered Beverage and 54% did not
order). Also note how the Food customers’
percentage per age group is almost
identical to the (Food + Beverage)
customers. Probably this is because the
Food + Beverage customers are the
dominating number where hardly any Food
items are being sold alone (as was
displayed in Exhibit #2).
Exhibit #4: Number of Customers (left)/ Number of Items Sold (right) as Distributed by Age
The following Exhibit displays the number
of customers (left) versus the number of
items sold (right) as distributed by age
(this time as a total). Note again how the
(20-30) age group has the largest number
of customers as well as the largest number
of orders (also the largest number of “no
order”).
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Customers
Items Ordered/Sold
Exhibit #5: Number of Customers (left)/ Number of Items Sold (right) as Distributed by Age
Exhibit #6 displays the number of
customers as distributed by age group, but
this time as categorized per shift. The left
of the Exhibit shows that distribution
during the evening shift and the middle
during the morning shift while the right is
during the afternoon shift. Interestingly,
the (20-30) year old customers are always
the largest number regardless of the shift
(Exhibit #5). However, during the evening
shift they are obviously much larger than
the other age groups whereas during the
Evening
morning shift, the (30-40) age group is
almost as large. The bottom part of the
Exhibit displays the data in terms of the
number of items ordered/sold again as
distributed by age group per each shift. All
Exhibits are displayed next to each other
for easy comparison; for example we can
say that the evening customers’ orders are
proportional to their size (top left & bottom
left), whereas the afternoon customers
order more items relative to their size (top
right
&
bottom
right).
Morning
Afternoon
Exhibit #6: Evening, Morning, Afternoon Number of Customers (top)/ Evening, Morning,
Afternoon Number of Items Ordered/Sold (bottom) as Distributed by Age
Case Description
Classification is one type of information
that could be obtained from data-mining to
help decision makers make predictions and
forecasts as well as devise campaigns and
promotions (Laudon, 2009). There are two
ways of analyzing the data of the number of
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customers who came and the number of
items sold. One way is to look at it with
respect to the
he day of the week
(Wednesday/ Thursday/ Friday), and the
other is from the perspective of the time of
the shift (evening/ morning/ afternoon).
Cilantro
The following Exhibit shows how the
Afternoon Cilantro customers are larger
than its competitors. This is only a relative
thing; the real large portion of customers
custom
is
during
the
Evening
shift.
Competitors
Exhibit #7: Total Number of Cilantro Customers Compared with the Total Number of the
Customers of the Competitors as Analyzed by the Different Shifts
It should be noted, however, that relatively
speaking, the Cilantro Evening shift is
almost 82% of its competitors. Since the
Evening shift constitute almost half of all
the customers watched, it is
i important to
try to make an attempt to attract more
Evening shift customers with the Evening
shift demand being so promising and the
total Evening shift
ft customers being so
numerous (as long as the size of the coffee
shop can accommodate more customers).
When
comparing
Cilantro’s
customers/items sold to all its competitors
as distributed by the time of the shift, we
see that the Evening shift has the largest
number of customers for Cilantro as well as
Competitor A and Competitor C.
C The same
goes for the number of Beverage items sold
where the Evening shift has the biggest
amount of Beverage items sold for Cilantro
as well as Competitor A and Competitor C.
However,
er, when focusing on the Food/
(Food + Beverage) items sold, we see the
Morning shift is doing very
ery well relative to
the other two shifts. The Afternoon shift is
relatively the weakest of all with the
smallest number of customers and the
minimal number of items sold. Again the
total of the three shifts is displayed in the
lower right part of the Exhibit
Exhi
as a
benchmark.
Exhibit #8: Evenings (top left) versus Mornings (top right) versus Afternoon (bottom
left) Number of Customers Along with Number of Items Sold at Cilantro as
Compared to Its Competitors (bottom right has the total of all shifts)
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Exhibit #9 displays the information in
terms of the day of the week for a quick
and easy comparison between the different
coffee shops. For Cilantro and its major
competitor (competitor A), the number of
customers as well as the number of items
sold is the largest on Thursday. For the
other competitors, the Wednesday number
of customers and number of items sold is
the largest. The total of the three days is
displayed in the lower right part of the
Exhibit
as
a
benchmark.
Exhibit #9: Wednesdays (top left) versus Thursdays (top right) versus Fridays (bottom
left) Number of Customers Along with Number of Items Sold at Cilantro as
Compared to Its Competitors (note that the bottom right has the total of all
shifts)
When focusing on the total regardless of
the day of the week and regardless of the
time of the shift, we can easily spot a major
difference between Cilantro and its
competitors where Cilantro has the largest
number of customers watched as well as
the largest number of items sold (see
Exhibit #10 at the top). Although Cilantro
has the biggest number of stores in the
country, analyzing this data in that way
could be misleading at times. Another way
to look at it is by analyzing the data relative
to the number of stores that were being
studied (Exhibit #10 in the middle). Here
Cilantro has five stores being studied and
Competitor A has three stores being
studied with only one store for all other
competitors For example having a total of
100 customers in the three stores put
together is better than having 100
customers in the five stores put together.
Further analysis gives more insight to the
meaning of all this. The bottom part of
Exhibit #10 displays the result of analyzing
the same data, but this time relative to the
size of the store. Here the size is also an
important factor since if say Cilantro has
100 customers in a store that can
accommodate only 100 is better than a
competitor having only 100 customers in a
store that could accommodate 200.
Cilantro Korba, for example, is doing very
well but when looking at totals only we
found out that one of its competitors is
superficially doing better only because it
has a bigger sized store and therefore could
accommodate
more
customers.
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Exhibit #10: Total Number of Customers Along with Number of Items Sold at Cilantro as
Compared to Its Competitors (top) Number of Customers Along with Number of Items
Sold Relative to the Number of Coffee Shops at Cilantro (5) as Compared to Competitor A
(3), Competitor B (1), Competitor C (1), and Competitor D (1) (middle) Number of
Customers Along with Number of Items Sold Relative to the Size of the Coffee Shops at
Cilantro (16) as Compared to Competitor A (9), Competitor B (4.5), Competitor C (2.5),
and Competitor D (5) (bottom)
The following Exhibit displays the same
information about all the coffee shops
combined and compared against Cilantro
but this time as differentiated by age group.
When looking at the first row it is easy to
note how the number of customers who
are in their twenties who were watched at
Cilantro (54%) is more than the total
watched (51%). On the other hand, when
focusing on the last row it is easy to detect
that Cilantro has 61% of its customers who
are in their twenties with “NO order”
(something that Cilantro top management
did not discourage).
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Total
Cilantro
Exhibit# 11: Number of Customers, Food Items, Beverage Items and Food+ Beverage Items
Sold
Now focusing on the Cilantro customers
only, and the different districts, we can
analyze the data from two different
perspectives:
the
weekdays versus
weekends or the time of the shift. The
results show that on weekdays the Cilantro
Korba district attracts the highest number
of customers, while the Cilantro Dowal on
Thursdays and Fridays was doing much
better (the left of Exhibit# 12). When
analyzing the data from the perspective of
the time of shift, the Cilantro Korba is also
doing very well in the evening shift while
Dowal is doing better in the afternoon shift
and Zamalek is the best in the morning
shift (the right of Exhibit # 12).
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Exhibit# 12: Number of Cilantro Customers in 5 Major Districts as Differentiated
between Weekdays/Weekends (left) and Time of Shifts (right)
The following Exhibit displays the number
of Cilantro customers by district (top) and
then it is detailed in terms of the day of the
week (Wednesdays versus Thursdays
versus Fridays), and finally by zooming in
on Wednesdays only in terms of the time of
the shift (bottom):
Exhibit # 13: (Cilantro Customers as Displayed by Districts and Further Examined by the
Day of the Week and Finally Detailed in Terms of Wednesdays Only)
The following two Exhibits display the
same information in terms of Thursdays
and
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Fridays
respectively:
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Exhibit #14: (Cilantro Customers as Examined by the Day of the Week and Further
Detailed in Terms of Thursdays Only)
The previous Exhibit showed that when
further data-mining was done to calculate a
customer’s probability to come on
Thursdays, we see that Cilantro City Stars
coupled with the afternoon shift has the
biggest number of customers. As for the
evening shift on Thursdays, Cilantro Dowal
and City Stars are doing great.
When focusing on Fridays only we see that
Cilantro Dowal coupled with the evening
shift has the biggest number of customers.
As for the morning shift, Cilantro Dowal
has the largest number of customers. This
is quite interesting as the morning shift is
the Friday prayer time in Egypt that should
be correlated with the demand. This
correlation is non-existent in the Dowal
district, contrarily to all the other districts
where demand significantly drops during
that
shift
(Exhibit
#15).
Exhibit # 15: (Cilantro Customers as Examined by the Day of the Week and Further
Detailed in Terms of Fridays Only)
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Current Challenges/Problems
the Organization
Facing
To analyze or rank coffee shop customers
according to their purchasing style and
pattern, the Rateb model is used (Rateb,
2013). This technique has been developed
by Rateb based of the Hughes’ RFM
(Hughes, 2010). Rateb’s ADI modeling
approach is based on the assumption that
the following three variables affect demand
(Exhibit #16):
Attribute (A):
the quality or
characteristic of the
customer (in terms of
number of items
bought per age group)
as related to the total
number of customers
in that particular age
group and not an
absolute number of
items bought.
Demographic (D):
the demand in coffee
shops (in terms of
number of customers)
per age group.
Item Distribution (I):the demand (in terms
of number of items)
per age group with
reference to a
particular item only
(Food versus
Beverage versus
Food+ Beverage)
Exhibit#16: The ADI Model (Rateb, 2013)
Getting information such as how recent,
frequent, or the amount of money paid
(RFM) for each coffee shop customer is not
at all practical for this kind of analysis and
that is why the ADI technique is used here.
The RFM and the ADI focus on past sale
that could be useful for the short run, but
attempting to collect the RFM data for, say
the teenager customers, would not be
useful in the coffee shop settings where the
customer could not be considered on an
individual level but rather on a group level
only (such as teenagers versus 60+).
Customers
are
assigned
Attribute,
Demographic, and Item Distribution (ADI)
scores based on total customers in a
particular age group, total items purchased
per age group and items purchased per
head/customer respectively (Exhibit #17).
The ADI uses the same concept of the 5
point Likert scale and takes into
consideration the significant influence of
each variable with one being the highest or
the best and five being the least influential.
The score is based on ranking the number
of customers (Demographic) into five
categories; so the age group with the
biggest number of customers receives a
(Demographic) ranking of 1. Similarly, an
(Item Distribution) ranking of 1 is assigned
to the age group that does the most
purchases.
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Exhibit#17: The ADI in 3D for All Coffee Shops
Food Items
The following Exhibit shows the ADI for the
food items for all the coffee shops then
Exhibit #18:
Cilantro alone and then all the Competitors
displayed respectively (as distributed by
age)
ADI for the Food Items for all Coffee Shops Watched (left), Cilantro Only
(middle), and All the Competitors (right) as Distributed by Age
The (Demographic) of the Child-Food
customer constitutes the least number
(score of 5 in Exhibit #18) as well as a
small portion of Food items bought by the
Child-Food customer (Item Distribution).
However, the (Attribute) score of 1 for the
Child customer indicates that its orders are
of the highest number of Food items per
head/Child customer. From these scores
we can interpret that despite the fact that
the Child’s overall Food items purchased is
comparably low (score of 4), he/she is a
good customer relative to the amount of
Food items ordered per head/customer.
Management should attempt to attract
more Child customers when selling Food.
When comparing this to the Cilantro versus
its Competitors we see that the
Competitors are doing slightly better only
in the total number of Child-Food
customers (Demographic
relative to the 5).
score
of
4
On the other hand, the (Demographic) as
well as the (Item Distribution) of the
teenager TA-Food customer constitutes a
very small portion of the total overall
customers (score of 4 for both variables).
With the (Attribute) score of 3, the TAFood customer is an OK customer but
probably management should not do
anything in the short run. When comparing
this to the Cilantro versus its Competitors
we see that the Competitors are doing
slightly better only in the total number of
TA-Food items sold (Item Distribution
score of 4 versus 5).
The (20-30)-Food customer constitutes the
highest portion of the total overall
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customers (Demographic) as well as the
highest portion of the Food items sold
(Item Distribution). However, with an
(Attribute) score of 3, makes the (20-30)
year Food customer an average OK
customer relative to the amount of Food
items ordered per head (almost one on
one). Management should attempt to upsell more Food to the (20-30) year
customer (note how the ADI score is the
same for Cilantro and its competitors in
Exhibit #18). Linking this to Exhibit #23,
we see that the (20-30)-Food customers
make up 40% and 34% for the number of
customers and items sold respectively!
The (31-40)-Food customer constitutes a
good portion of the overall Food customers
(Demographic) as well as more than just
OK portion relative to the (Item
Distribution). With an (Attribute) score of
2, the (31-40) year customer is a good Food
customer ordering a relatively high amount
of Food items per head/person (note how
the Demographic and scores for Cilantro
are worse than its competitors in Exhibit
#18 but are better for the Item
Distribution).
The (41-50)-Food customer constitutes a
very small portion of the total overall Food
customers (Demographic) as well as an
average portion of the (Item Distribution).
However, the (Attribute) score of 1 for the
(41-50)-Food customer indicates that its
orders are of the highest number of Food
items per head/customer. From these
scores we can interpret that despite the
fact that the (41-50) customers are only a
small portion of the overall Food
customers, they are good customers
relative to the amount of Food items
ordered per head. Management should
attempt to attract more (41-50) customers
when selling Food items (note how the
Demographic and Item Distribution scores
for Cilantro are the same as its competitors
in Exhibit #18).
The (51-60) and the (60+) year customers
constitute a very small portion of the total
overall customers (Demographic) as well
as the number of Food items bought (Item
Distribution). However, the (Attribute)
score of 1 for the (51-60) and the (60+)
customers indicates that their orders are of
the highest number of Food items per
head/customer. From these scores we can
interpret that despite the fact that the (5160) and the (60+) customers constitute a
very small portion of the overall Food
customers &/or items ordered, they are
good customers relative to the amount of
Food items ordered per customer/head.
Management should attempt to attract
more (51-60) and (60+) customers when
selling Food items (note how in Exhibit
#18 the Attribute of the (60+)-Food
Cilantro customers is 1 versus 2 for its
Competitors which indicates that Cilantro
is doing a better job in terms of Food items
ordered per head for this age group).
Beverage, and Food + Beverage Items
The following three Exhibits display the
ADI for the Beverage, Food + Beverage, and
NO order items respectively (as distributed
by age). The previous analysis for the Food
more or less prevails for the Beverage. In
Exhibit #19 we see how the (20-30)
Beverage customers have identical scores
with the Food customers from the previous
Exhibit. The rest have very minor
differences. For example, the Beverage is
selling less for the (TA) customers as well
as the (31-40) customers. As for the
(Attribute) Beverage score, it is worse for
the (41-50) Beverage customers of the
competitors and better for the 60+ of the
competitors. Despite the fact that the
difference is minor it is very important to
look closely into this data because the
Beverage items give more net profit per
item than the Food items (almost three
times as much).
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D. F. Rateb (2013), IBIMA Business Review, DOI: 10.5171/2013.942446.
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Exhibit #19:
ADI for the Beverage Items for all Coffee Shops Watched (left), Cilantro
Only (middle), and All the Competitors (right) as Distributed by Age
Exhibit #20: ADI for the Food + Beverage Items for all Coffee Shops Watched (left),
Cilantro Only (middle), and All the Competitors (right) as Distributed by Age
NO-order Items
For the NO order items, the ADI scores and
the analysis are also very important.
However, a score of 5 for the
(Demographic) is considered a good thing
in Exhibit #21 contrasted with the previous
three exhibits where it was considered a
weak number. This is because a score of 5
for the Child-NO order customer means
that hardly any Child customer sits but
does not order (whereas a 5 for the ChildBeverage customer means that there aren’t
many Child-Beverage customers –Exhibit
#19). Back to Exhibit #21 we see that the
(20-30) customers have a 1 under
(Demographic) which indicates that those
are the most customers who do not place
orders (compatible to Exhibit #23 where
those customers make 54% of the NOorder customers). When comparing the
(Demographic) of Cilantro against its
Competitors in that same exhibit, we see
that Cilantro is doing relatively better than
its Competitors with the (TA)-NO order
customers and the (31-40)-NO order
customers. In other words, the (TA) and
the (31-40) Cilantro customers who do not
order are fewer than their competitors’
counterparts (less NO-order).
Exhibit #21: ADI for the NO Order Items for all Coffee Shops Watched (left), Cilantro Only
(middle), and All the Competitors (right) as Distributed by Age
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D. F. Rateb (2013), IBIMA Business Review, DOI: 10.5171/2013.942446.
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IBIMA Business Review
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It is important to note how in Exhibit #21
the number of items sold (Item
Distribution) is not filled; because this is
the NO-order exhibit meaning we cannot
count the number of items that have not
been ordered /sold yet.
Exhibit #22:
To analyze the Attribute score of the NOorder it was important to benchmark this
against the previous three exhibits. The
following exhibit is merely the Attribute
data extracted from the previous 4 exhibits
of the Food, Beverage, Food + Beverage,
and NO order exhibits (Exhibits #18, 18,
19, and 20 respectively).
The Attribute (A) Score for all Coffee Shops Watched (left), Cilantro Only
(middle), and All the Competitors (right) as Distributed by Age
Interestingly we see that the Attribute
score for the (51-60) customers is 1 for the
Food, Beverage, Food + Beverage, and even
the NO-order items (Exhibit #22). This
means that despite the fact that the orders
of the (51-60) customers are of the highest
number of Food, Beverage, Food +
Beverage items per head/customer, the
(51-60) customer also have the highest
number of NO-order items per head. This is
because the ADI technique is a vertical
analysis per age group customer; so a high
Food score indicating a high number of
Food items ordered per head in that age
group is a relative thing (number of items
ordered in that age group relative to the
number of customers in that age group).
Whereas for the NO order ADI score we
cannot see how many NO order items there
are relative to the NO order customers
(how can we count the items that were not
ordered?). In this case it is simply the
number of NO-order customers in a
particular age group relative to the total
number of customers in that age group.
Note how the (41-50) customers also have
the highest Attribute for the Food,
Beverage, Food + Beverage items per head,
but an average Attribute of 3 for the NOorder score; indicating that they do not
usually sit without placing an order. The
same goes for the Child customers, but the
Child of the competitor does less NO-order
than that of Cilantro’s. For the (TA)
customers, Cilantro is doing much better
than its competitors where most (TA)
customers of the competitors do not place
orders. For the (20-30) customers, the
competitors are doing better with fewer
NO-orders taking place per head as
opposed to those of Cilantro. This is lost
business for Cilantro because the (20-30)
customers constitute the biggest portion of
Beverage customers as well as Beverage
items sold in general as can be seen from
Exhibit #23. Beverage, as previously noted,
give a much higher net profit per item than
that of the Food. Also Beverage has much
more customers and much more items sold
as was previously displayed and discussed
in Exhibit #2.
From Exhibit #23 we can see how the (2030) customers are by far the largest in
terms of the number as well as the total
items purchased (40%/34%, 50%/42%,
and 39%/33% for Food, Beverage, and
Food + Beverage respectively). The (20-30)
customers are also the ones with the
highest percentage of those who do not
place orders (54%)! Second to that come
the (31-40) customers but they are also
among the highest who do not place orders
(20% NO-order). It is up to Management to
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D. F. Rateb (2013), IBIMA Business Review, DOI: 10.5171/2013.942446.
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decide what to do with this data. For
example, Cilantro openly allows customers
to come in without placing orders while
some of its competitors do not allow this
and when done it is simply “under the
table”. Trying to up sell Food/Beverage
items to the (20-30) customers will
definitely be the ultimate goal of all those
coffee shops but the approach might differ.
When the coffee shop is crowded it needs a
different approach than when it is not for
those customers who although ranked the
highest with the ADI scores, also have the
highest number of NO-order. In other
words, there is no one strategy or data
mining technique that fits all – neither at
the coffee shop level nor at the day-to-day
level. Combining the ADI with the previous
analysis supports decision making and data
mining especially when major changes
takes place. For example, as previously
stated, the data collected was before the
Egyptian revolution, whereas after the
uprising the Evening shift significantly
decreased in terms of the number of
customers (obvious even to the naked eye).
Exhibit #23: The Percentage of Customers and Items Sold as Distributed by Age
Conclusion
Rateb’s ADI (2013) is à la Hughes’s RFM
(2010), but the emphasis is on coffee shop
items purchased per customer as
categorized by age (A), the total number of
customers (D) in each age group, and the
total items purchased by each age group
customer (I); instead of the recency (R),
frequency (F), and money (M). The ADI
thus focuses on the attitudes and lifestyles
of the coffee shop customers as segmented
by age groups; which is very useful in
forecasting trends in the long run; and
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D. F. Rateb (2013), IBIMA Business Review, DOI: 10.5171/2013.942446.
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thus, it supports decision making by simply
delivering a summary of customer
behavior and purchasing patterns. That is
why the ADI technique is the one used in
this study.
This research used the ADI technique to
analyze the data in terms of the Food items
sold, the Beverage items, the Food +
Beverage, as well as the NO-orders as
categorized by age groups. Further analysis
was done to compare the Cilantro business
against its coffee shop competitors. Padhy
et al., (2012) warn against trying to come
up with one data mining system that fits all.
The data mining techniques used in this
study could help generate new business
opportunities by providing: prediction of
trends and behaviors as well as discovery
of previously unknown or hidden patterns.
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